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Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.


Papers
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01 Jan 1994
TL;DR: The handwritten signature has many purposes and meanings; underlying these related purposes there are two ideas: the handwritten signature is a distinctive personal mark and because it is distinctive, its authenticity can be verified; both these ideas can be called into question.
Abstract: The handwritten signature has many purposes and meanings; underlying these related purposes there are two ideas: the handwritten signature is a distinctive personal mark, because it is distinctive, its authenticity can be verified. Both these ideas can be called into question. A handwritten signature is distinctive and verifiable only if forgery is detectable and hence impracticable. It is illuminating to contrast what an electronically-captured signature might be with its paper equivalent. Needless to say, a paper signature is bulky to store and difficult to retrieve. Much of the visual verification which is currently done is cursory and depends on subjective criteria; it is totally impracticable to apply such techniques to large volumes of signatures. Finally, the data captured on paper are relatively few. By contrast, dynamic signature capture using a digitizer gives us stroke order, pen speed and acceleration as well as the image itself. This gives the opportunity to perform biometric tests to determine those characteristic aspects of an individual's performance which are unique to him. The use of such data for verification purposes leaves a forger with more and bigger hurdles to surmount. >

7 citations

Journal ArticleDOI
TL;DR: A vocabulary recognition-model optimization method is proposed based on a similar phoneme–recognition process and efficient feature extraction to recognize models adjacent to the model group.
Abstract: In processing voice with environment noise, the noise must be eliminated to improve the vocabulary recognition rate. In this process, noise elimination and feature extraction for model-estimate technologies are utilized. Concerning these noise-elimination and model-estimate technologies, the most important part is to estimate mixed noise in the source signal and eliminate it. In a vocabulary recognition system, if unexpected noise appears in the signal, or if quantization noise is basically added to digital signals, the source signal is changed or damaged, which decreases the recognition rate. If a source signal is transformed or changed by being mixed with diverse kinds of noise, the hidden Markov model (HMM) is used for effective noise elimination. The HMM forms a model by extracting features to flexibly respond to diverse vocabulary changes found in voice and text, etc. The method is applicable to data changing over time, and can establish a more effective model as the number of parameters constituting the model grows larger. It can provide a robust model estimate by using a parameter set for structured models. HMM-based vocabulary recognition shows discriminating distribution of recognition probability regarding recognition vocabulary models, and has lower computational complexity for recognition. But it produces a relatively lower recognition rate. To solve that problem, a vocabulary recognition-model optimization method is proposed based on a similar phoneme---recognition process and efficient feature extraction. In vocabulary recognition, a similar phoneme---recognition process is applied to HMM to recognize models adjacent to the model group. Efficient feature extraction is used to optimize the recognition model to enhance the recognition rate. For vocabulary composition, a Gaussian-mixture feature-extraction model is optimized and used as a vocabulary recognition model. Then, it is processed with similar-phoneme recognition regarding the vocabulary recognition model.

7 citations

Journal ArticleDOI
31 Oct 2016
TL;DR: This paper provides an authentication technology for verifying dynamic signature made by finger on smart phone using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature.

7 citations

Proceedings ArticleDOI
18 Nov 1991
TL;DR: Results of handwritten-letter recognition experiments show that the proposed architecture has the ability to recognize a deformed target pattern in an original image with much noise, especially lumped noises.
Abstract: The authors present a novel pattern recognition architecture using three-layered backpropagation (BP) models. The proposed architecture consists mainly of the following two completely separate functions: extraction of a target pattern and recognition of the extracted pattern. It is possible that the proposed architecture detects where and what the target pattern is. In order to realize these functions, the following networks are introduced: filtering network, position network, size network, frame-working network, and categorizing networks. Results of handwritten-letter recognition experiments show that the proposed architecture has the ability to recognize a deformed target pattern in an original image with much noise, especially lumped noises. >

7 citations

Proceedings Article
01 Nov 2012
TL;DR: The problem is formulated as finding the Viterbi path in a Markov model, since the consistent recognition results can be thought of as the most likely sequence of the states and the proposed multilevel object recognition system is implemented.
Abstract: In this study, we address the issue on multilevel object recognition. The multilevel object recognition is object recognition in various levels, that is, simultaneous recognition of its instance, category, material, etc. At each level, many recognition methods have been proposed in the literature. Therefore it is straightforward to design a multilevel object recognition system using conventional methods independently. However, these “levels” are related each other and form hierarchical structure. Hence the recognition performance can be improved by considering consistency of the recognition results at all levels. To model the consistency, we formulate the problem as finding the Viterbi path in a Markov model, since the consistent recognition results can be thought of as the most likely sequence of the states. We implemented the proposed multilevel object recognition system and evaluated it to show validity.

7 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832